The Accessibility Challenge & Rising Regulatory Pressure
I once spent weeks manually navigating with the NVDA screen reader, checking color contrasts with external tools, and documenting issues in spreadsheets — all to ensure accessibility.
It was exhausting, but it showed me just how demanding traditional accessibility testing can be. That experience taught me firsthand about the complexities companies face when trying to make their applications truly accessible or to comply with regulatory requirements.
Why Web Accessibility Compliance Is Hard
Companies often struggle with accessibility compliance due to several interconnected challenges:
- Manual testing with tools like NVDA, JAWS, or VoiceOver requires specialized knowledge and significant time investment.
- Lengthy audit cycles — A single comprehensive audit/testing can take several weeks to a month, during which development teams are left waiting for feedback.
- Bottlenecks in release cycles — Accessibility testing often becomes a bottleneck in release cycles, delaying product launches while teams struggle through manual verification processes.
- High costs — The cost of specialized accessibility consultants and companies that offer these services, along with the time required for thorough testing, is often too high for many organizations to afford, particularly smaller ones.
Meanwhile, the risk of non-compliance grows daily as accessibility regulations become more stringent worldwide. The European Accessibility Act enforcement began on June 28, 2025, mandating that all e-commerce websites, mobile applications, and digital services serving EU customers must comply with WCAG 2.2 Level AA standards.
Non-compliance can result in fines of up to €3 million, market exclusion, and suspension of business operations. The Act covers all entities offering digital products and services in the EU market, regardless of their home country location.
Similar accessibility regulations are emerging worldwide, including the United States’ ADA Title II rule, Canada’s Accessible Canada Act, Australia’s Disability Discrimination Act updates, and enhanced accessibility requirements in Japan and South Korea.
Introducing AI-Powered Accessibility Testing
This is where the AI-powered accessibility testing framework can transform the landscape entirely. At Levi9, we’ve developed and refined this in-house approach, positioning us to help clients automate accessibility audits, accelerate releases, and maintain compliance.
Rather than replacing human expertise, this system amplifies it — combining the reliability of established accessibility tools with the analytical power of artificial intelligence to create a comprehensive, efficient, and insightful testing solution.
High-Level Architecture and Design
The framework integrates three industry-standard accessibility testing tools with AI analysis capabilities, creating a powerful automated workflow that delivers both depth and actionable insights.
The testing process follows an orchestrated sequence designed for both thoroughness and efficiency:
1. Initial Scanning Phase: The system launches a headless browser instance and navigates to the target URL. Once the page loads completely, all three accessibility tools execute simultaneously: axe-core analyzes the DOM structure for violations, Lighthouse performs its comprehensive audit including performance impacts on accessibility, and Pa11y validates against WCAG guidelines. This parallel execution typically completes within 20–30 seconds per page.
2. AI Analysis Phase: Raw results feed into the AI analysis engine, which applies sophisticated reasoning to understand issue relationships, assess user impact across different disability types, and map findings to specific WCAG success criteria. The AI distinguishes between issues that completely block users versus those that create friction, enabling intelligent prioritization based on actual user impact rather than simple severity ratings.
3. Report Generation Phase: The system produces comprehensive reports tailored to different audiences. Executives can receive high-level dashboards showing compliance scores and business risk assessments. Developers get detailed technical guidance with specific code examples and implementation strategies to fix the issues.
Customization Capabilities
One of the framework’s great strengths lies in its flexibility and customization options, allowing organizations to tailor the system to their specific needs and constraints.
AI Model Selection: The system supports various local AI models running via Ollama, each optimized for different use cases. Lighter models like Llama 3.2:3b provide rapid analysis for development environments, while more powerful models like Llama 3.1:70b deliver deeper insights for comprehensive audits. Organizations can balance speed versus analytical depth based on their specific requirements.
Browser Configuration: Organizations can customize browser settings including viewport sizes for mobile testing, user agent strings for device-specific analysis, headless or non-headless running of the browser depending on the situations and timeout configurations for complex single-page applications.
Report Customization: The template-based reporting system allows organizations to modify report layouts, add custom branding, include organization-specific compliance requirements, and integrate with existing documentation systems. Reports can focus on specific WCAG levels (A, AA, or AAA) based on organizational compliance targets.
Understanding Report Results and Next Steps
The framework generates results that bridge the gap between technical findings and actionable business intelligence. Understanding these results and knowing how to act on them is crucial for maximizing the system’s value.
Report Interpretation: Each report provides multiple layers of information. The executive summary offers compliance scores (ranging from 0–100%), WCAG conformance levels (AA, Partial AA, A, or Non-compliant), and risk assessments showing potential legal and user experience impacts. Technical sections detail specific violations with code examples, affected user groups, implementation complexity estimates, and detailed raw results from the accessibility tools executions.
Prioritization Strategy: AI analysis prioritizes issues using a sophisticated scoring method that considers user impact, implementation effort, and business risk. Critical issues that completely block access for users with disabilities receive highest priority, followed by high-impact issues affecting specific user groups, then moderate improvements that enhance overall usability.
Action Planning: Teams should focus on immediate fixes for critical issues, typically missing alt text, color contrast failures, and keyboard navigation problems. These often require minimal development effort but provide maximum accessibility improvement. Medium-term goals should address structural issues like heading hierarchies and ARIA implementation, while long-term planning can focus on comprehensive design system improvements.
Conclusion
Having spent countless hours with screen readers, manually checking contrast ratios, and struggling to prioritize fixes, I can confidently say this AI-powered framework setup represents a paradigm shift in how we can approach accessibility assurance.
The combination of comprehensive automated testing, intelligent AI analysis, and actionable reporting transforms accessibility from a challenging compliance burden into a manageable, systematic process.
Organizations can now achieve WCAG compliance thorough accessibility coverage in hours rather than weeks, while gaining deeper insights into user impact and implementation strategies. At Levi9, we’re building expertise in AI-powered accessibility frameworks, equipping us to help clients ensure compliance while boosting efficiency.
With accessibility regulations enforcing strict compliance deadlines and substantial penalties for non-compliance, the question isn’t whether organizations need robust accessibility testing — it is whether they can afford to continue with manual, time-intensive approaches when automated, intelligent solutions are already available.
The question is no longer whether to adopt these tools, but how soon you can implement them.